AI-Powered Java Applications with Spring AI 2.0 & Spring Boot 4

Instructor Photo

Faisal Memon

Software engineer, Product, Entrepreneur

The future of software isn't AI or Java. It's AI inside Java.

Every enterprise is racing to put intelligence into its products: chatbots that know company data, assistants that take real actions, and systems that reason, search, and respond in real time. Most of those products run on the JVM, and most AI courses are taught in Python.

This course closes that gap. You'll learn to build AI-powered applications with Spring AI 2.0, Spring Boot 4, and Java, using the same patterns production teams use to ship reliable, observable, and secure AI features.

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🔥 PRODUCTION-READY, NOT TUTORIAL-READY. This isn't a "call an API and print the response" course. You'll go deep on the parts most courses skip, the ones that separate a demo from a real product.

Go deep on what separates a demo from a real product

🎯 What You'll Master

  • Spring AI Advisors - The interception layer behind logging, memory, RAG, and guardrails. Most courses barely touch them; here you'll build your own.
  • Streaming responses - Server-Sent Events and Reactor Flux, so your UI feels instant
  • Structured output - Turn LLM text into type-safe Java objects
  • Retrieval-Augmented Generation (RAG) - From ingestion to production, grounded in your own data
  • Tool calling & AI agents - Agents that take real actions inside your system
  • Model Context Protocol (MCP) - The emerging standard for connecting AI to tools and data

🛠️ What You'll Build

You'll start with your first ChatClient call and progress to a complete AI-powered application: a streaming chatbot backend with memory, semantic search, RAG over your own documents, and tool-using agents, connected to a ready-made frontend you can download and run.

One real project that implements everything you learn in the course.

🔌 Model-Agnostic by Design

You'll work with OpenAI, Anthropic Claude, Google Gemini, and local open-source models via Ollama, and learn how to swap providers without rewriting your application. That flexibility matters when your company changes vendors, costs, or compliance rules.

📦 What's Inside

  • LLM fundamentals explained for Java developers (tokens, context windows, temperature, and more)
  • Spring AI ChatClient, prompt templates, and prompt chaining
  • Structured output with BeanOutputConverter, ListOutputConverter, and MapOutputConverter
  • Streaming AI responses with SSE, WebFlux, and Flux
  • The Spring AI advisor chain, plus custom advisors for timing, logging, and more
  • Chat memory for multi-turn conversations
  • Embeddings, vector stores, and semantic search with pgvector
  • A complete RAG section covering production concerns
  • Tool calling and function calling in Java
  • Building AI agents with Spring AI
  • MCP for Java developers
  • LangChain4j essentials, so you understand the other major Java AI framework
  • A capstone: a full-stack streaming AI chatbot
AI-Powered Java Applications with Spring AI 2.0 & Spring Boot 4 Course

Build production-ready AI applications in Java: advisors, streaming, RAG, tool calling, agents, and MCP.

Enroll for free with Udemy For Business Subscription

Note: This offer won't last long!

What You Get After Enrolling?

  • Build a full-stack streaming AI chatbot with memory, RAG, and tool-using agents
  • Master Spring AI 2.0 on Spring Boot 4
  • Work with OpenAI, Claude, Gemini, and local models via Ollama
  • RAG with embeddings and pgvector, grounded in your own data
  • AI agents and MCP for Java developers
  • Lifetime access with a 30-day refund policy, in accordance with Udemy's terms and conditions

Course Curriculum

  1. Welcome & Orientation - How the course is structured, the project you'll build, and setting up your environment
  2. AI Foundations - How LLMs work, explained for Java developers: tokens, context windows, temperature, and more
  3. Spring AI Foundations - ChatClient, providers, streaming with SSE and Flux, and structured output into Java objects
  4. Prompt Engineering for Production APIs - Prompt templates, prompt chaining, and prompts that hold up behind a real API
  5. Spring AI Advisors - The advisor chain, and building your own advisors for timing, logging, and guardrails
  6. Chat Memory with LLMs - Giving your application memory for multi-turn conversations
  7. Memory and Conversation State - Managing and persisting conversation state across sessions and users
  8. Embeddings and Vector Search - Embeddings, vector stores, and semantic search with pgvector
  9. RAG: Retrieval Augmented Generation - From document ingestion to production, grounded in your own data
  10. Tool Use and Function Calling - Letting the model call your Java code to take real actions
  11. Building Agents with Spring AI - Agents that reason, plan, and act inside your system
  12. MCP for Java Developers - Connecting AI to tools and data with the Model Context Protocol
  13. 🏗️ Real Project: A full-stack streaming AI chatbot that brings together everything you've learned: memory, semantic search, RAG over your own documents, and tool-using agents, connected to a ready-made frontend.

Who This Course Is For

Requirements

Who's Teaching You

I'm Faisal, founder of EmbarkX. Thousands of developers have learned Java, Spring Boot, microservices, and cloud-native engineering through EmbarkX courses. This course brings that same production-first approach to AI engineering.

Why Now?

AI engineering is quickly becoming a core expectation for backend developers. Java developers who can integrate LLMs, RAG, and agents into Spring applications are in a rare position: they already know how to build systems enterprises trust. This course gives you the AI half.

Enroll now and start building the next generation of intelligent Java applications.